Parallel point cloud compression using truncated octree
| Autor: | Pradeep Kumar Jayaraman, Naimin Koh, Jianmin Zheng |
|---|---|
| Přispěvatelé: | School of Computer Science and Engineering, 2020 International Conference on Cyberworlds (CW) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Truncation Node (networking) Unstructured point cloud Point cloud Compression 020207 software engineering Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology 01 natural sciences Computer Science::Robotics 010309 optics Octree Geometric Modeling Compression (functional analysis) Morton code 0103 physical sciences Compression ratio 0202 electrical engineering electronic engineering information engineering Computer Graphics Parallel processing Geometric modeling Algorithm Data compression |
| Zdroj: | CW |
| Popis: | Existing methods of unstructured point cloud compression usually exploit the spatial sparseness of point clouds using hierarchical tree data structures for spatial encoding. However, such methods can be inefficient when very deep octrees are applied to sparse point cloud data to maintain low level of geometric error during compression. This paper proposes a novel octree structure called truncated octree that improves the compression ratio by representing the deep octree with a set of shallow sub-octrees which can save storage without losing the original structure. We also propose a variable length addressing scheme, to adaptively choose the length of an octree’s node address based on the truncation level—shorter (resp. longer) address when octree is truncated near the leaf (resp. root) which leads to further compression. The method is able to achieve 40% to 90% compression ratio on our tested models for point clouds of different spatial distributions. For extremely sparse point clouds, the method achieves approximately 7 times higher compression ratio than previous methods. Moreover, the method is designed to run in parallel for octree construction, encoding and decoding. Ministry of Education (MOE) National Research Foundation (NRF) Accepted version This research was supported by National Research Foundation (NRF) Singapore, under its Virtual Singapore (VSG) Programme (Award No. NRF2015VSG-AA3DCM001-018). It was also supported by the Ministry of Education, Singapore, under its MoE Tier-2 Grant (MoE 2017-T2-1-076). |
| Databáze: | OpenAIRE |
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